Introduction
Hello! my name is Sophia, I am a molecular biologist working in proteomics, currently a Postdoctoral fellow. Before Data School, I coded on Macro (Excel). I used to spend a lot of time cleaning and tidying protein data, I always felt like I could do it faster if I had programming skills. These weeks learning R have changed my daily work. The possible things that we could do have opened my mind to a new perspective of my research.
My Project
I am working in the study of ryegrass, a potential source for gluten peptides contamination. Gluten refers to a class of storage proteins found in cereal grains, including wheat, rye, barley, and oats. Consumption of these gluten proteins leads to an autoimmune response in the case of coeliac disease. Previous studies have identified gluten-like proteins in ryegrass. Since this is a common weed found in grain fields, there is a possibility of cross-contamination. First of all, I need to characterize the gluten proteins and peptides with ryegrass origen. To do so I have performed Data-dependent mass spectrometric analysis. The results of this study provided identification of 3162 proteins, and 8231 peptides. Now what I need to do is found how many of them are gluten-like.
Preliminary results
I will analyse the amino acids composition of the proteins of my database. Since gluten proteins have a high composition of the amino acids Glutamine (Q) and Proline (P). I will search for all proteins that have over 20% Glutamine.
Tables| N | Accession | Name | Sequence |
|---|---|---|---|
| 1 | spP4910614331_MAIZE | 14-3-3-like | MASAELSREENVYMAKLAEQAERYEEMVEFMEKVAKTVDSEELTVEERNLLSVAYKNVIGARRASWRIISSIEQKEEGRGNEDRVTLIKDYRGKIETELTKICDGILKLLETHLVPSSTAPESKVFYLKMKGDYYRYLAEFKTGAERKDAAENTMVAYKAAQDIALAELAPTHPIRLGLALNFSVFYYEILNSPDRACSLAKQAFDEAISELDTLSEESYKDSTLIMQLLRDNLTLWTSDISEDPAEEIREAPKRDSSEGQ |
| 2 | spQ84Q72HS181_ORYSJ | 18.1 | MSLIRRSNVFDPFSLDLWDPFDGFPFGSGSRSSGSIFPSFPRGTSSETAAFAGARIDWKETPEAHVFKADVPGLKKEEVKVEVEDGNVLQISGERSKEQEEKTDKWHRVERSSGKFLRRFRLPENTKPEQIKASMENGVLTVTVPKEEPKKPDVKSIQVTG |
| 3 | spP69555PSBH_WHEAT | Photosystem | MATQTVEDSSKPRPKRTGAGSLLKPLNSEYGKVAPGWGTTPFMGVAMALFAIFLSIILEIYNSSVLLDGILTN |
| 4 | spP36886PSAK_HORVU | Photosystem | MASQLSAMTSVPQFHGLRTYSSPRSMATLPSLRRRRSQGIRCDYIGSSTNLIMVTTTTLMLFAGRFGLAPSANRKATAGLKLEARESGLQTGDPAGFTLADTLACGAVGHIMGVGIVLGLKNTGVLDQIIG |
| 5 | spQ6YZE2GSA_ORYSJ | Glutamate-1-semialdehyde | MAGAAAASAAAAAVASGISARPVAPRPSPSRARAPRSVVRAAISVEKGEKAYTVEKSEEIFNAAKELMPGGVNSPVRAFKSVGGQPIVFDSVKGSRMWDVDGNEYIDYVGSWGPAIIGHADDTVNAALIETLKKGTSFGAPCVLENVLAEMVISAVPSIEMVRFVNSGTEACMGALRLVRAFTGREKILKFEGCYHGHADSFLVKAGSGVATLGLPDSPGVPKGATSETLTAPYNDVEAVKKLFEENKGQIAAVFLEPVVGNAGFIPPQPGFLNALRDLTKQDGALLVFDEVMTGFRLAYGGAQEYFGITPDVSTLGKIIGGGLPVGAYGGRKDIMEMVAPAGPMYQAGTLSGNPLAMTAGIHTLKRLMEPGTYDYLDKITGDLVRGVLDAGAKTGHEMCGGHIRGMFGFFFTAGPVHNFGDAKKSDTAKFGRFYRGMLEEGVYLAPSQFEAGFTSLAHTSQDIEKTVEAAAKVLRRI |
| Note: 5 examples of proteins found in the database. The column Sequence indicates the amino acids (letter code) that make up each protein. |
Look for amino acids Q and P.
library(tidyverse)
library(Biostrings)
my_database_comp <- mutate(my_database, totalAA= str_count(Sequence, '\\w'), Qcomp= str_count(Sequence,'Q')) %>%
mutate(Q100 = Qcomp *100 / totalAA) %>%
mutate(Pcomp= str_count(Sequence,'P')) %>%
mutate(P100= Pcomp *100/totalAA)
my_database_comp[,'Q100']=round(my_database_comp[,'Q100'],2)
my_database_comp[,'P100']=round(my_database_comp[,'P100'],2)| N | Accession | Name | totalAA | Qcomp | Q100 | Pcomp | P100 |
|---|---|---|---|---|---|---|---|
| 1 | spP4910614331_MAIZE | 14-3-3-like | 261 | 6 | 2.30 | 7 | 2.68 |
| 2 | spQ84Q72HS181_ORYSJ | 18.1 | 161 | 4 | 2.48 | 12 | 7.45 |
| 3 | spP69555PSBH_WHEAT | Photosystem | 73 | 1 | 1.37 | 5 | 6.85 |
| 4 | spP36886PSAK_HORVU | Photosystem | 131 | 5 | 3.82 | 5 | 3.82 |
| 5 | spQ6YZE2GSA_ORYSJ | Glutamate-1-semialdehyde | 478 | 8 | 1.67 | 27 | 5.65 |
|
Note: totalAA = Number of total amino acids of the protein Qcomp= Number of Glutamine found in the protein Q100= Porcentage of Glutamine in the protein Pcomp= Number of Proline found in the protein P100= Porcentage of Proline in the protein |
Working with Protein Data
Figure 1: Glutamine and Proline composition in Ryegrass
My Digital Toolbox
To work with Protein Databases, that are usually in the format .fasta. I have used the package Biostrings. For tyding the data,Tidyverse (my new best friend) and plyr.
Other packages I have used are: dplyr and stringr. For visualization ggplot and gganimate.
Favourite tool
My favorite package is tidyverse. Only with learning a few functions in the first days of Data School already made my daily work much easier. It was love at second sight. I was able to clean and tidy my data. The functions I used the most are mutate, join and of course pipe %>%.
Moreover, another of my favorite parts of working with R is using Regex, learn this was so useful for making scripts.
My time went …
tidying protein data manually.
parts of your project take the most time and effort? Were there any surprising challenges you encountered, and how did you solve them?
Next steps
I will keep working with R, i think there is a lot I havent try yet. I want to practice more creating functions. In the future I will like to create a script that identifies non-gluten proteins, proteins that are similar or that generates and immune reaction on patients. But for that I will have to learn more things in R, like working with API.
And maybe some day start with python……
My Data School Experience
This summary is mostly about your project. However we would also like to hear about other parts of your Data School experience. What aspects of the program did you really enjoy? Have you tried applying the skills you have learned in your daily work? Have you been able to transfer this knowledge to your team members? Any descriptions of the personal impact the program has had are welcome here as well!